Abstract
Recent advancements in edge hardware enabled increasingly complex artificial intelligence workloads to be executed directly on resource-constrained devices. However, single-edge devices remain computationally limited compared with hybrid or cloud alternatives. This work presents a fully edge-based architecture implementing agentic AI on heterogeneous devices for home surveillance with a natural language interface, in which analysis tasks are parallelized across the distributed devices. The system is tested on a two-node prototype: a Raspberry Pi 5 with a Hailo-10H accelerator and an STM32MP257FDK board. Dynamic leader election allows recovery from the loss of any single node, including the leader, with graceful degradation. Qwen3-1.7B achieved 93% tool-selection accuracy over 24 tools and maintained robust summarization performance, outperforming FunctionGemma-270M. The jina-reranker-v2-base-multilingual cross-encoder performs comparably to the tested LLMs at tool selection on small pool sizes (i.e., with few tools to choose from). Hardware accelerators improved inference energy efficiency by 2.0x to 17.3x, while leader execution incurred less than 2 W of additional power. The main bottleneck is model latency rather than network overhead, and KV-cache reuse proved essential to meet the latency constraint on the STM32MP257FDK. The proposed prototype coordinates inference across heterogeneous edge devices to answer queries, with the weaker node exhibiting scalability limitations.
